1. Field of Invention
The present invention relates to an orthogonal-frequency-division-multiplexing wireless system (OFDM wireless system), and particularly to an antenna-array-based multiple-input multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) system. The MIMO-OFDM system uses QR decomposition of the MIMO channel matrix to parameterize the channel state information (CSI).
2. Description of the Related Art
The wireless mobile communication today is capable of carrying mega transmission data and this has become a standard requirement in the modern society. To more effectively increase the channel capacity thereof, today's wireless mobile communication employs a so-called antenna-array-based MIMO-OFDM technology. In a MIMO-OFDM system with a closed loop, a spatial vector-coding scheme is used at the transmitter thereof, which can largely increase the channel capacity. However, the effectiveness of the spatial vector-coding scheme depends on the correctness of the estimated MIMO channel state information (CSI) at the receiver of the system. Therefore, the parameters and information fed back from the receiver to the transmitter must be sufficient for the transmitter to reproduce the CSI, even more so when the number of the transmitting antenna is larger than the number of the receiving antenna.
Among the currently available spatial vector-coding schemes, a preferred solution provides the maximum mutual information of the MIMO channels by means of singular value decomposition (SVD) of the MIMO channel matrix H for each frequency band to parameterize the CSI. The SVD can be expressed by:
      H    ⁢          →      SVD        ⁢          U      ⁢                          ⁢      Σ      ⁢                          ⁢              V        *              ,And, by implementing a Givens rotation on the V matrixes of all the frequency bands for producing, a plurality of Givens rotation matrixes Gp,q(θ,φ) can be generated. Such transformation can be expressed by:
  V  ⁢      →    GivensRotations    ⁢            G      1        ⁢          G              2        ⁢                                        ⁢    …    ⁢                  ⁢                  G                                                            (                                                      2                    ⁢                                          M                      T                                                        -                  1                                )                            ⁢                              M                R                                      -                          M              R              2                                2                    .      Afterwards, the Givens rotation matrix information of all the frequency bands are fed back to the transmitter, where the Givens rotation matrixes are combined for re-obtaining the V matrixes of all the frequency bands. Then, the V matrixes of all the frequency bands are applied to the vector coding at the transmitter. In the above-described solution scheme, the right/left singular vectors of the MIMO channel matrixes are used as the transmit/receive weighting vectors, and a water-filing power allocation is used to establish the optimum spatial multiplexing system.
Obviously, the above-described scheme has the such disadvantages as too much computation, high complexity and excessive feedback data or feedback rate. It is because the receiver needs to conduct computations to obtain the V matrixes from the MIMO channel matrixes H and then to conduct computations to obtain the Givens rotation matrixes from the V matrixes for each frequency band, so that the computation and high complexity are inevitable. In addition, the Givens rotation matrix information of all the frequency bands is required to be fed back, therefore the feedback information is proportional to the number of frequency bands. For 512 frequency bands, a typical number of frequency bands, the disadvantage of excessive feedback data or feedback rate is also inevitable. For example, if the channel matrix H has a dimension of 2×4, 62 complex numbers are needed to conduct a multiplication for each frequency band and at least 10 real numbers are required to be fed back, then the total feedback information reaches as high as 5,120 real numbers.